U.S. patent application number 15/579228 was filed with the patent office on 2018-06-28 for non-invasive method for monitoring patient respiratory status via successive parameter estimation.
The applicant listed for this patent is KONINKLIJKE PHILIPS N.V.. Invention is credited to ANTONIO ALBANESE, NICOLAS WADIH CHBAT, NIKOLAOS KARAMOLEGKOS, FRANCESCO VICARIO, DONG WANG.
Application Number | 20180177963 15/579228 |
Document ID | / |
Family ID | 56116481 |
Filed Date | 2018-06-28 |
United States Patent
Application |
20180177963 |
Kind Code |
A1 |
WANG; DONG ; et al. |
June 28, 2018 |
NON-INVASIVE METHOD FOR MONITORING PATIENT RESPIRATORY STATUS VIA
SUCCESSIVE PARAMETER ESTIMATION
Abstract
A Moving Window Least Squares (MWLS) approach is applied to
estimate respiratory system parameters from measured air flow and
pressure. In each window, elastance E.sub.rs (or resistance
R.sub.rs) is first estimated, and a Kalman filter may be applied to
the estimate. This is input to a second estimator that estimates R
(or E), to which a second Kalman filter may be applied. Finally,
the estimated E.sub.rs and R.sub.rs are used to calculate muscle
pressure P.sub.mus(t) in the time window. A system comprises a
ventilator (100), an airway pressure sensor (112), and an air flow
sensor (114), and a respiratory system analyzer (120) that performs
the MWLS estimation. Estimated results may be displayed on a
display (110) of the ventilator or of a patient monitor. The
estimated P.sub.mus(t) may be used to reduce patient-ventilator
dyssynchrony, or integrated to generate a Work of Breathing (WOB)
signal for controlling ventilation.
Inventors: |
WANG; DONG; (SCARSDALE,
NY) ; VICARIO; FRANCESCO; (BOSTON, MA) ;
ALBANESE; ANTONIO; (NEW YORK, NY) ; KARAMOLEGKOS;
NIKOLAOS; (NEW YORK, NY) ; CHBAT; NICOLAS WADIH;
(WHITE PLAINS, NY) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
KONINKLIJKE PHILIPS N.V. |
EINDHOVEN |
|
NL |
|
|
Family ID: |
56116481 |
Appl. No.: |
15/579228 |
Filed: |
June 1, 2016 |
PCT Filed: |
June 1, 2016 |
PCT NO: |
PCT/IB2016/053206 |
371 Date: |
December 4, 2017 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
62169863 |
Jun 2, 2015 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61M 16/0677 20140204;
A61B 5/085 20130101; A61B 5/7203 20130101; A61M 2230/46 20130101;
A61M 2230/06 20130101; A61B 5/7225 20130101; A61M 2205/3303
20130101; A61M 2230/435 20130101; A61B 5/725 20130101; A61M
2205/502 20130101; A61M 2230/42 20130101; A61M 2230/205 20130101;
A61M 2205/505 20130101; A61B 5/087 20130101; A61B 5/742 20130101;
A61M 2016/0027 20130101; A61M 2230/432 20130101; A61M 2202/0208
20130101; A61M 2016/0036 20130101; A61M 16/026 20170801; A61M
2202/0208 20130101; A61M 2202/0007 20130101; A61M 2230/435
20130101; A61M 2230/005 20130101 |
International
Class: |
A61M 16/00 20060101
A61M016/00; A61B 5/085 20060101 A61B005/085; A61B 5/087 20060101
A61B005/087; A61B 5/00 20060101 A61B005/00 |
Claims
1. A medical ventilator device comprising: a ventilator configured
to deliver ventilation to a ventilated patient; a pressure sensor
configured to measure the airway pressure P.sub.y(t) of the
ventilated patient; an air flow sensor configured to measure the
air flow {dot over (V)}(t) into and out of the ventilated patient;
and a respiratory system analyzer comprising a microprocessor
configured to estimate respiratory parameters of the ventilated
patient using moving time window least squares (MWLS) estimation
including (i) respiratory system elastance or compliance (E.sub.rs
or C.sub.rs), (ii) respiratory system resistance (R.sub.rs), and
(iii) respiratory muscle pressure (P.sub.mus(t)).
2. The medical ventilator device of claim 1 wherein the MWLS
estimation in cludes, for each time window of the MWLS estimation,
performing the following operations in order: (1) estimating one of
(i) elastance or compliance and (ii) resistance; (2) estimating the
other of (i) elastance or compliance and (ii) resistance using the
estimated value from operation (1); and (3) estimating respiratory
muscle pressure using the values estimated in operations and.
3. The medical ventilator device of claim 2 wherein the operation
estimates elastance or compliance and the operation estimates
resistance using the estimated value of elastance or compliance
from operation.
4. The medical ventilator device of claim 2 wherein the operation
optimizes elastance E.sub.rs, resistance R.sub.rs, and the
difference .DELTA.P.sub.mus of the respiratory muscle pressure
P.sub.mus of the equation: .DELTA.P.sub.y(t)=R.sub.rs.DELTA.{dot
over (V)}(t)+E.sub.rs.DELTA.V(t)+.DELTA.P.sub.mus with respect to
the measured values of P.sub.y(t) and {dot over (V)}(t) in the time
window of the MWLS where V(t)=.intg.{dot over (V)}(t)dt and
.DELTA.P.sub.y(t)=P.sub.y(t)-P.sub.y(t-1) and .DELTA.{dot over
(V)}(t)={dot over (V)}(t)-{dot over (V)}(t-1) and
.DELTA.V(t)=V(t)-V(t-1).
5. The medical ventilator device of claim 4 wherein the operation
optimizes the respiratory muscle pressure P.sub.mus(t) and one of
elastance E.sub.rs and resistance R.sub.rs of the equation:
P.sub.y(t)=R.sub.rs{dot over (V)}(t)+E.sub.rsV(t)+P.sub.mus(t) with
respect to the measured values of P.sub.y(t) and {dot over (V)}(t)
in the time window of the MWLS with the estimated value from
operation held fixed and P.sub.mus(t) modeled by a parameterized
function of time.
6. The medical ventilator device of claim 5 wherein P.sub.mus(t) is
modeled by a polynomial function of time.
7. The medical ventilator device of claim 6 wherein operation is
repeated with P.sub.mus(t) modeled by zeroeth, first, and second
order polynomial functions of time and the optimized elastance
E.sub.rs or resistance R.sub.rs of the three repetitions are
combined.
8. The medical ventilator device of claim 5 wherein the operation
estimates respiratory muscle pressure as P.sub.y(t)-{circumflex
over (R)}.sub.rs{dot over (V)}(t)-E.sub.rsV(t) in the time window
of the MWLS where {circumflex over (R)}.sub.rs and E.sub.rs are
estimated values from operations and.
9. The medical ventilator device of claim 2 wherein one or both of
the operations and includes applying a Kalman filter to the
estimated value.
10. The medical ventilator device of claim 9 wherein one or both of
the operations and further includes generating an uncertainty
metric for the estimated value based on a noise variance of the
Kalman filter.
11. The medical ventilator device of claim 1 further comprising: a
display configured to display one or more of the respiratory
parameters of the ventilated patient estimated by the respiratory
system analyzer.
12. The medical ventilator device of claim 1 wherein the ventilator
is programmed to adjust positive air pressure output by the
ventilator in synch with increasing or decreasing magnitude of the
respiratory muscle pressure (P.sub.mus(t)) in order to reduce
patient-ventilator dyssynchrony.
13. The medical ventilator device of claim 1 wherein: the
respiratory system analyzer is configured to estimate a work of
breathing (WoB) as WoB=.intg.P.sub.mus(t)dV(t) where P.sub.mus(t)
is the respiratory muscle pressure as a function of time estimated
using the MWLS estimation; and the ventilator is programmed to
control mechanical ventilation provided by the ventilator to
maintain the estimated WoB at a setpoint WoB value.
14.-19. (canceled)
20. A non-transitory storage medium storing instructions readable
and executable by an electronic data processing device to perform a
method operating on measurements of airway pressure P.sub.y(t) and
air flow {dot over (V)}(t) of a patient on a ventilator, the method
including: applying moving window least squares (MWLS) estimation
to estimate (i) respiratory system elastance E.sub.rs, (ii)
respiratory system resistance R.sub.rs, and (iii) respiratory
muscle pressure P.sub.mus(t), wherein: the MWLS estimation (i)
comprises fitting: .DELTA.P.sub.y(t)=R.sub.rs.DELTA.{dot over
(V)}(t)+E.sub.rs.DELTA.V(t)+.DELTA.P.sub.mus to .DELTA.P.sub.y(t)
to obtain values for E.sub.rs, R.sub.rs, and .DELTA.P.sub.mus,
where .DELTA.P.sub.y(t) is a difference signal of the measured
airway pressure, .DELTA.{dot over (V)}(t) is a difference signal of
the measured air flow, .DELTA.V(t) is a difference signal of
respiratory system air volume V(t)=.intg.{dot over (V)}(t)dt, and
.DELTA.P.sub.mus is a constant, and the MWLS estimation (ii)
comprises fitting: P.sub.y(t)=R.sub.rs{dot over
(V)}(t)+E.sub.rsV(t)+P.sub.mus(t) to obtain values for R.sub.rs and
P.sub.mus(t), where E.sub.rs is set the value determined in the
MWLS estimation (i) and with P.sub.mus(t) is approximated as a
parameterized function, and the MWLS estimation (iii) comprises
evaluating: P.sub.mus=P.sub.y(t)-R.sub.rs{dot over
(V)}(t)-E.sub.rsV(t) where E.sub.rs is set the value determined in
the MWLS estimation (i) and R.sub.rs is set the value determined in
the MWLS estimation (ii).
21. The non-transitory storage medium of claim 20 wherein the
respiratory system elastance E.sub.rs is represented in the MWLS
estimation operations as a respiratory system compliance
C.sub.rs=1/E.sub.rs.
Description
FIELD
[0001] The following relates generally to systems and methods for
monitoring and characterizing respiratory parameters during patient
ventilation. It finds particular application in a system to provide
real-time diagnostic information to a clinician to personalize a
patient's ventilation strategy and improve patient outcomes and
will be described with particular reference thereto. However, it is
to be understood that it also finds application in other usage
scenarios and is not necessarily limited to the aforementioned
application.
BACKGROUND
[0002] Real-time assessment of the respiratory system's parameters
(resistance R.sub.rs and compliance C.sub.rs) and patient's
inspiratory effort (respiratory muscle pressure P.sub.mus(t))
provides invaluable diagnostic information for clinicians to
optimize ventilation therapy.
[0003] A good P.sub.mus(t) estimation can be used to quantify
patient's inspiratory effort and select the appropriate level of
ventilation support in order to avoid respiratory muscle atrophy
and fatigue. Moreover, the estimated P.sub.mus(t) waveform can also
be used for triggering and cycling off the ventilator so as to
reduce patient-ventilator dyssynchrony. Estimates of R.sub.rs and
C.sub.rs are also important, as they provide quantitative
information to clinicians about the mechanical properties of the
patient's respiratory system and they can be used to diagnose
respiratory diseases and better select the appropriate ventilator
settings.
[0004] P.sub.mus(t) is traditionally estimated via esophageal
pressure measurement. This technique is invasive, in the sense that
a balloon needs to be inserted inside the patient's esophagus, and
moreover, not reliable when applied during long periods in
intensive care conditions.
[0005] Another option to estimate P.sub.mus(t) is to calculate it
based on the Equation of Motion of the Lungs. Assuming R.sub.rs and
C.sub.rs are known, it is indeed possible to estimate P.sub.mus(t)
via the following equation, known as the Equation of Motion of the
lungs:
P y ( t ) = R rs V . ( t ) + ( 1 C rs ) V ( t ) + P mus ( t ) + P 0
( 1 ) ##EQU00001##
where P.sub.y(t) is the pressure measured at the Y-piece of the
ventilator, {dot over (V)}(t) is the flow of air into and out of
the patient's respiratory system (measured again at the Y-piece),
V(t) is the net volume of air delivered by the ventilator to the
patient (measured by integrating the flow signal {dot over (V)}(t)
over time), P.sub.0 is a constant term to account for the pressure
at the end of expiration (needed to balance the equation but not
interesting per se) and will be considered as part of P.sub.mus(t)
in the following discussion. However, R.sub.rs and C.sub.rs have to
be measured or estimated first.
[0006] R.sub.rs and C.sub.rs may be estimated by applying the
flow-interrupter technique (also called End Inspiratory Pause,
EIP), which however interferes with the normal operation of the
ventilator, or under specific conditions where the term
P.sub.mus(t) can be "reasonably" assumed to be zero (i.e. totally
unload patient's respiratory muscles). These conditions include:
periodic paralysis in which the patient is under Continuous
Mandatory Ventilation (CMV); periodic high pressure support (PSV)
level; specific portions of each PSV breath that extend both during
the inhalation and the exhalation phases; and exhalation portions
of PSV breaths where the flow signal satisfies specific conditions
that are indicative of the absence of patient's inspiratory
effort.
[0007] R.sub.rs and C.sub.rs estimation using the EIP maneuver has
certain drawbacks and relies on certain assumptions. The EIP
maneuver interrupts the normal ventilation needed by the patient.
It also assumes that patient respiratory muscles are fully relaxed
during the EIP maneuver in order for the R.sub.rs and C.sub.rs
computation to be valid. Further, the R.sub.rs and C.sub.rs
estimates obtained via the EIP maneuver, which affect the estimate
of P.sub.mus(t) on the following breath, are assumed to be constant
until the next EIP maneuver is executed, so that continuous and
real-time estimates of R.sub.rs and C.sub.rs are not obtained. In
practice, changes in patient's conditions can occur in between two
consecutive EIP maneuvers, and this would jeopardize the estimate
of P.sub.mus(t). A further disadvantage is that the static maneuver
(EIP) is performed in a specific ventilation mode (Volume Assisted
Control, VAC) and the obtained values for R and C might not be
representative of the true values that govern the dynamics of the
lungs in other ventilation modes, such as Pressure Support
Ventilation (PSV). Therefore, the accuracy of P.sub.mus(t) computed
via equation (1) during PSV operation can be compromised.
[0008] The above mentioned estimation methods operate on the
assumption that P.sub.mus(t) is negligible. Implementation of this
assumption can be problematic in clinical settings. For example,
imposing periodic paralysis and CMV on a patient is generally not
clinically feasible. Similarly, imposing periodic high PSV
interferes with the normal operation of the ventilator and may not
be beneficial to the patient. The assumption of negligible
P.sub.mus(t) during PSV breaths is debatable, especially during the
inhalation phase. Approaches which operate on a chosen portion of
the respiration cycle also limit the fraction of data points that
are used in the fitting procedure, which makes the estimation
results more sensitive to noise.
[0009] In the following, non-invasive methods are disclosed for
monitoring patient respiratory status via successive parameter
estimation, which overcome various foregoing deficiencies and
others.
SUMMARY
[0010] In accordance with one aspect, a medical ventilator device
is described. The device includes a ventilator configured to
deliver ventilation to a ventilated patient, a pressure sensor
configured to measure the airway pressure P.sub.y(t) at a Y-piece
of the ventilator and an air flow sensor configured to measure the
air flow {dot over (V)}(t) into and out of the ventilated patient
at the Y-piece of the ventilator. The device also comprises a
respiratory system monitor comprising a microprocessor configured
to estimate respiratory parameters of the ventilated patient using
moving window least squares (MWLS) estimation including (i)
respiratory system elastance or compliance (E.sub.rs or C.sub.rs),
(ii) respiratory system resistance (R.sub.rs), and (iii)
respiratory muscle pressure (P.sub.mus(t)).
[0011] In accordance with another aspect, a method comprises:
ventilating a patient using a ventilator; during the ventilating,
measuring airway pressure P.sub.y(t) and air flow {dot over (V)}(t)
of air into and out of the patient; using a microprocessor,
applying moving window least squares (MWLS) estimation to estimate
(i) the patient's respiratory system elastance or compliance
E.sub.rs or C.sub.rs, (ii) the patient's respiratory system
resistance R.sub.rs, and (iii) the patient's respiratory muscle
pressure P.sub.mus(t); and displaying on a display one or more of
the respiratory parameters of the patient estimated by applying
MWLS estimation.
[0012] One advantage resides in providing a non-invasive method for
monitoring patient respiratory status via successive parameter
estimation including resistance, compliance, and respiratory muscle
pressure.
[0013] Another advantage resides in providing a ventilator with
improved data analysis.
[0014] Still further advantages of the present invention will be
appreciated to those of ordinary skill in the art upon reading and
understand the following detailed description. It is to be
appreciated that none, one, two, or more of these advantages may be
achieved by a particular embodiment.
BRIEF DESCRIPTION OF THE DRAWINGS
[0015] The disclosure may take form in various components and
arrangements of components, and in various steps and arrangement of
steps. The drawings are only for purposes of illustrating the
preferred embodiments and are not to be construed as limiting the
invention.
[0016] FIG. 1 illustrates a ventilation system for use on a patient
with the proposed ventilation estimation scheme.
[0017] FIG. 2 illustrates a block diagram of the described
estimation scheme.
[0018] FIG. 3 illustrates a moving window least squares algorithm
for the E.sub.rs estimation.
[0019] FIG. 4 illustrates a moving window least squares algorithm
example of the polynomial order of the local P.sub.mus(t)
waveform.
[0020] FIG. 5 illustrates the maximum ratio combination for the
MWLS R.sub.rs estimation results.
DETAILED DESCRIPTION
[0021] The following relates to characterization of respiratory
parameters during patient ventilation and in particular to the
respiratory muscle pressure P.sub.mus(t), respiratory resistance
R.sub.rs, and respiratory compliance C.sub.rs or elastance
E.sub.rs=1/C.sub.rs. In principle, these parameters can be
estimated using the Equation of Motion of the Lungs (Equation (1)),
which relates these parameters to the pressure P.sub.y(t) at the
ventilator mouthpiece and the air flow {dot over (V)}(t), along
with the air volume in the lungs V(t)=.intg.{dot over (V)}(t)dt. In
practice, because the respiratory muscle pressure P.sub.mus(t)
varies over time, estimating P.sub.mus(t), R.sub.rs, and E.sub.rs
jointly using the Equation of Motion of the Lungs is generally
underdetermined and cannot be analytically solved. Various
approaches to dealing with this include measuring additional
information using invasive probes, or creating "special case"
circumstances by operations such as interrupting normal breathing.
Invasive probes have apparent disadvantages, while techniques that
rely upon manipulating normal patient breathing cannot provide
continuous monitoring of normal respiration and may be detrimental
to the patient.
[0022] With reference to FIG. 1, a medical ventilator system
includes a medical ventilator 100 that delivers air flow at a
positive pressure to a patient 102 via an inlet air hose 104.
Exhaled air returns to the ventilator 100 via an exhalation air
hose 106. A Y-piece 108 of the ventilator system serves to couple
air from the discharge end of the inlet air hose 104 to the patient
during inhalation and serves to couple exhaled air from the patient
into the exhalation air hose 106 during exhalation. Note the
Y-piece 108 is sometimes referred to by other nomenclatures, such
as a T-piece. Not shown in FIG. 1 are numerous other ancillary
components that may be provided depending upon the respiratory
therapy being received by the patient 102. Such ancillary
components may include, by way of illustration: an oxygen bottle or
other medical-grade oxygen source for delivering a controlled level
of oxygen to the air flow (usually controlled by the Fraction of
Inspired Oxygen (FiO.sub.2) ventilator parameter set by the
physician or other medical personnel); a humidifier plumbed into
the inlet line 104; a nasogastric tube to provide the patient 102
with nourishment; and so forth. The ventilator 100 includes a user
interface including, in the illustrative example, a touch-sensitive
display component 110 via which the physician, respiratory
specialist, or other medical personnel can configure ventilator
operation and monitor measured physiological parameters and
operating parameters of the ventilator 100. Additionally or
alternatively, the user interface may include physical user input
controls (buttons, dials, switches, et cetera), a keyboard, a
mouse, audible alarm device(s), indicator light(s), or so forth. It
is also noted that the illustrative ventilator 100 is merely an
illustrative example.
[0023] The illustrative ventilator 100 is a dual-limb ventilator
with proximate sensors. However, the disclosed patient respiratory
status monitoring techniques may be employed in conjunction with
substantially any type of ventilator, such as with a single-limb or
dual-limb ventilator, a ventilator having valves or blower, a
ventilator with an invasive coupling to the patient (e.g. via a
tracheostomy or endotracheal tube) or a ventilator with a
noninvasive coupling to the patient (e.g. using a facial mask), a
ventilator with proximal sensors for measuring pressure and flow as
illustrated or a ventilator without such proximal sensors that
relies upon sensors in the ventilator unit, or so forth.
[0024] With continuing reference to FIG. 1, the patient 102 is
monitored by various physiological parameter sensors. In
particular, FIG. 1 illustrates two such sensors: an airway pressure
sensor 112 that measures pressure P.sub.y (t) at the coupling to
the patient (usually measured at the Y-piece 108, hence P.sub.y
(t)) and an air flow sensor 114 that measures air flow {dot over
(V)}(t) to or from the patient (also usually measured at the
Y-piece 108). The sensors 112, 114 may be integrated into the
Y-piece 108, interposed on the air lines 104, 106, or integrated
into the ventilator 100. During mechanical ventilation, other
physiological parameters may be monitored by suitable sensors, such
as heart rate, respiratory rate, blood pressure, blood oxygenation
(e.g. SpO.sub.2), respiratory gases composition (e.g. a capnograph
measuring CO.sub.2 in respiratory gases), and so forth. Other
physiological parameters may be derived from directly measured
physiological parameters.
[0025] The system further includes a respiratory system analyzer
120 comprising a microprocessor, microcontroller, or other
electronic data processing device programmed to process input data
including the airway pressure P.sub.y (t) and air flow {dot over
(V)}(t) to generate information about the patient respiratory
system parameters: resistance R.sub.rs, compliance C.sub.rs (or,
equivalently, elastance E.sub.rs=1/C.sub.rs), and the patient's
inspiratory effort characterized as a function of time by the
respiratory muscle pressure P.sub.mus(t). These parameters are
determined as a function of time, in real-time, by evaluating the
Equation of Motion of the Lungs (Equation (1)) using moving window
least squares estimation (MWLS) applied to the airway pressure
P.sub.y (t) and air flow {dot over (V)}(t) along with the air
volume V(t)=.intg.{dot over (V)}(t)dt determined from {dot over
(V)}(t) by an air flow integrator 122. (Alternatively, a dedicated
air volume sensor may be employed). To overcome the underdetermined
nature of Equation (1), the MWLS estimation is performed using
successive estimation of: (1) the elastance or compliance (E.sub.rs
or C.sub.rs) parameter via an E.sub.rs estimator 132; followed by
(2) estimation of the resistance (R.sub.rs) parameter via an
R.sub.rs estimator 134; followed by (3) estimation of the
respiratory muscle pressure (P.sub.mus(t)) parameter via a
P.sub.mus(t) estimator 136.
[0026] These successive estimators 132, 134, 136 are applied within
the time window 130 which is generally of duration two seconds or
less, and more preferably of duration one second or less, and in an
illustrative example of duration 0.6 seconds with data sampling at
100 Hz so that the time window contains 60 samples. An upper limit
on the duration of the time window is imposed by the respiration
rate, which for a normal adult is typically 12 to 20 breaths per
minute corresponding to a breathing cycle of duration 3-5 seconds.
The duration of the time window 130 is preferably a fraction of the
breathing cycle duration so that the parameters E.sub.rs and
R.sub.rs can be reasonably assumed to be constant within each time
window 130, and variation of P.sub.mus(t) within each time window
130 can be represented using a relatively simple approximation
function (e.g. a low-order polynomial in the illustrative examples
disclosed herein).
[0027] The estimators 132, 134, 136 are successively applied within
each time window 130, and for each successive (and partially
overlapping) time interval 130 (hence the term "moving" time
window), to provide estimation of E.sub.rs, R.sub.rs, and
P.sub.mus(t) in real time. In the illustrative examples, the values
of E.sub.rs and R.sub.rs are assumed to be constant within each
time window 130, so that the estimation of these parameters is in
real-time with a time resolution comparable to the duration of the
time window 130, e.g. two second or less in some embodiments, or
more preferably one second or less, and 0.6 seconds in the
illustrative examples. If successive time windows partially
overlap, this can further improve the effective time resolution.
The real-time estimation of P.sub.mus(t) can be of higher temporal
resolution than E.sub.rs and R.sub.rs, since variation of
P.sub.mus(t) with time within the time window 130 is, in the
illustrative examples, modeled by a low-order polynomial function
of time.
[0028] The approach disclosed herein leverages the recognition
that, of the three parameters being estimated, the
elastance/compliance (E.sub.rs or C.sub.rs) generally varies most
slowly over time. In the Equation of Motion of the Lungs (Equation
(1)), E.sub.rs is the coefficient of the air volume V(t) which, as
an integral, varies slowly over time. The next most slowly varying
parameter is generally the resistance R.sub.rs, which is the
coefficient of the air flow {dot over (V)}(t). Finally, the
respiratory muscle pressure P.sub.mus(t) has the potential to vary
most rapidly over time as it changes in response to the patient
actively inhaling and exhaling. In view of this, the illustrative
examples of the P.sub.mus(t) estimator 136 do not assume
P.sub.mus(t) is a constant within the time window 130, but instead
employ a low-order approximation polynomial function. Instead of a
low-order polynomial approximation of P.sub.mus(t) within the time
window 130, in other contemplated embodiments some other
parameterized function of time is contemplated, such as a spline
function.
[0029] With continuing reference to FIG. 1, the outputs E.sub.rs
(or C.sub.rs), R.sub.rs, and P.sub.mus(t) can be used for various
purposes. In one application, one or more of the estimated
parameters may be displayed on the display component 110 of the
ventilator 100, for example as a numeric real-time value and/or as
a trend line plotted as a function of time. Typically, the
respiratory elastance or compliance (E.sub.rs or C.sub.rs) and the
respiratory resistance (R.sub.rs) are of most interest to the
clinician and are suitably displayed and/or trended. The
respiratory muscle pressure P.sub.mus(t) is a waveform acquired as
a function of time in real-time during normal clinically operative
mechanical ventilation accordingly, P.sub.mus(t) can be used by the
ventilator 100 for triggering and cycling off the mechanical
ventilation so as to reduce patient-ventilator dyssynchrony (that
is, to synchronize application of positive pressure by the
ventilator 100 with the inhalation portion of the patient's
respiratory muscle action).
[0030] In some embodiments, a work of breathing (WoB) estimator 140
integrates the respiratory muscle pressure P.sub.mus(t) over
volume, i.e. WoB=.intg.P.sub.mus (t)dV(t). The WoB is a metric of
how much effort the patient 102 is applying to breathe on his or
her own. The WoB may be displayed and/or trended on the display
component 110 to provide the clinician with useful information for
setting ventilator pressure settings in ventilation modes such as
Pressure Support Ventilation (PSV). Moreover, since the WoB
estimator 140 provides WoB in real-time (e.g. with a time lag and
resolution on the order of a second or less in some embodiments)
the ventilator 100 optionally employs the WoB as a feedback control
parameter, e.g. adjusting controlled ventilator settings to
maintain the WoB at a constant set-point value. For example, if the
WoB increases, this implies the patient 102 is struggling to
breathe and accordingly the positive pressure applied by the
ventilator 100 in PSV mode should be increased to provide the
struggling patient with increased respiration assistance.
[0031] With reference to FIG. 2, some illustrative embodiments of
the successive estimators 132, 134, 136 are described. successive
estimation of the parameters E.sub.rs, R.sub.rs, and P.sub.mus(t)
over a time window of a fraction of a second over which the
parameters E.sub.rs and R.sub.rs are assumed to be constant is
shown. In the first pass (performed by the E.sub.rs estimator 132),
all three parameters E.sub.rs, R.sub.rs, and .DELTA.P.sub.mus(t)
are assumed to be constant over the time window 130 and are
computed simultaneously--but only the estimated E.sub.rs is
retained from this first pass. (In notation used herein, the
overscript "hat", i.e. {circumflex over (p)}, is used to indicate
the estimated value of parameter p.) In a second pass (performed by
the R.sub.rs estimator 134), the contribution of the now known
(estimated) E.sub.rs is removed by subtraction, and the remaining
portion of the Equation of Lung Motion is fitted for R.sub.rs and
P.sub.mus(t), the latter being approximated using a low order
polynomial (n=0, 1, or 2). In experiments, it was found that the
best choice of polynomial order is dependent upon the respiratory
phase at which the time window 130 is located due to possible
overfitting--as respiratory phase is not known a priori, in
illustrative embodiments disclosed herein a weighted combination of
polynomials of zeroeth, first, and second order is used. The output
of the R.sub.rs estimator 134 is the estimated value of the
respiratory resistance, i.e. {circumflex over (R)}.sub.rs. Finally,
in a third pass (performed by the P.sub.mus(t) estimator 136), the
contribution of the now known (estimated) {circumflex over
(R)}.sub.rs is removed by further subtraction, and the remaining
portion of the Equation of Lung Motion is directly fitted to obtain
the estimated respiratory muscle pressure, i.e. {circumflex over
(P)}.sub.mus (t).
[0032] With continuing reference to FIG. 2, the illustrative
E.sub.rs estimator 132 is further described. At 208 a difference
operation is performed on the airway pressure P.sub.y(t) and the
output .DELTA.P.sub.y(t) is calculated as .DELTA.P.sub.y(t)=P.sub.y
(t)-P.sub.y (t-1). A Moving Window Least Squares (MWLS) estimator
is used to at 210 to continuously estimate E.sub.rs(t)--which is
the respiratory system's elastance, E.sub.rs(t)=1/C.sub.rs(t)--and
is based on the following difference equation:
.DELTA.P.sub.y(t).apprxeq.R.sub.rs.DELTA.{dot over
(V)}(t)+E.sub.rs.DELTA.V(t)+.DELTA.P.sub.mus
It should be noted that E.sub.rs(t) is estimated as a function of
time insofar as the estimate E.sub.rs is generated for each time
window 130, so that the time function E.sub.rs(t) is generated as
the value E.sub.rs for successive time windows 130 as successive
(partially overlapping) time windows are applied over time. However
inside each time window 130, .DELTA.P.sub.mus(t), the difference
signal of the P.sub.mus(t) waveform, and the parameters R.sub.rs(t)
and E.sub.rs(t) are modeled as constants and jointly estimated by a
least squares minimization method. For the E.sub.rs estimator 132,
only the estimate of E.sub.rs(t), namely E.sub.rs, is used (after
filtering by a Kalman filter 212 in the illustrative example of
FIG. 2), while the other estimation outputs are discarded.
Moreover, the E.sub.rs estimator 132 also calculates the variance
of the estimate E.sub.rs, denoted herein as
.delta..sub.E.sub.rs.
[0033] The input to the MWLS estimator 210 is the difference signal
of P.sub.y(t), that is, .DELTA.P.sub.y(t), which is output by the
difference operation 208. Based on the equation of the motion
(Equation (1)), .DELTA.P.sub.y(t) can be modeled as:
.DELTA.P.sub.y(t).apprxeq.R.sub.rs(t).DELTA.{dot over
(V)}(t)+E.sub.rs(t).DELTA.V(t)+.DELTA.P.sub.mus(t)
where .DELTA.{dot over (V)}(t)={dot over (V)}(t)-{dot over
(V)}(t-1) is the flow difference signal,
.DELTA.V(t)=V(t)-V(t-1)={dot over (V)}(t)T is the volume difference
signal (where T is the sampling time interval, e.g. sampling at 100
Hz corresponds to T=0.01 sec), and
.DELTA.P.sub.mus(t)=P.sub.mus(t)-P.sub.mus(t-1) is the P.sub.mus(t)
difference signal.
[0034] In the following, the size (or duration) of the sliding time
window 130 is denoted as L, which is optionally a system parameter
that can be set by the user. The sliding window at a current time t
spans the interval [t-L+1, t]. For the MWLS estimator 210, the
P.sub.mus(t) difference signal, .DELTA.P.sub.mus(t), in the sliding
window is modeled as a constant, .DELTA.P.sub.mus. It is further
assumed that R.sub.rs and E.sub.rs are constant in the sliding time
window 130. Therefore, the equation for .DELTA.P.sub.y(t)
becomes:
.DELTA.P.sub.y(t).apprxeq.R.sub.rs.DELTA.{dot over
(V)}(t)+E.sub.rs.DELTA.V(t)+.DELTA.P.sub.mus
At time t, the MWLS algorithm 210 uses the input signals in the
sliding window 130, that is to say, the samples .DELTA.P.sub.y(n)
and {dot over (V)}(n) in the interval t-L+1.ltoreq.n.ltoreq.t, to
estimate R.sub.rs, E.sub.rs, and .DELTA.P.sub.mus jointly, but only
the E.sub.rs estimate E.sub.rs is used in the subsequent operations
(i.e. the subsequent estimators 134, 136).
[0035] As further shown in FIG. 3, specifically, at time t, the
MWLS formulation solves the least square problem 300 based on the
equation described above:
[0036] At time t,
.DELTA.P.sub.y(t).apprxeq.R.sub.rs.DELTA.{dot over
(V)}(t)+E.sub.rs.DELTA.V(t)+.DELTA.P.sub.mus
[{tilde over (E)}.sub.rs,{tilde over (R)}.sub.rs,.DELTA.{tilde over
(P)}.sub.mus].sup.T=(X.sup.TX).sup.-1X.sup.TY
Y=[.DELTA.P.sub.y(t),.DELTA.P.sub.y(t-1), . . .
,.DELTA.P.sub.y(t-L+1)].sup.T
X=[x(t),x(t-1), . . . ,x(t-L+1)].sup.T
x(t)=[.DELTA.V(t),.DELTA.{dot over (V)}(t),1].sup.T
.delta..sub.{tilde over
(E)}.sub.rs=(X.sup.TX).sup.-1(1,1)*.delta..sub..DELTA.P.sub.y
Moreover, the variance of the E.sub.rs estimate, .delta..sub.{tilde
over (E)}.sub.rs, is also calculated, where
.delta..sub..DELTA.P.sub.y is the least square residual
variance,
.delta..sub..DELTA.P.sub.y=(Y-X[{tilde over (E)}.sub.rs,{tilde over
(R)}.sub.rs,.DELTA.{tilde over
(P)}.sub.mus].sup.T).sup.T(Y-X[{tilde over (E)}.sub.rs,{tilde over
(R)}.sub.rs,.DELTA.{tilde over (P)}.sub.mus].sup.T)/L
[0037] As indicated in FIG. 3, the MWLS estimation 210 is performed
continuously as the moving window moves forward, e.g. window
310.sub.n succeeded by next window 310.sub.n+1, and so forth. Since
the MWLS method is sensitive to the P.sub.y measurement noise and
the modelling error, only the estimate of E.sub.rs, {tilde over
(E)}.sub.rs, is retained by the E.sub.rs estimator 132 and the
other estimate outputs (e.g. {tilde over (R)}.sub.rs and
.DELTA.{tilde over (P)}.sub.mus) are discarded.
[0038] To further improve the E.sub.rs estimation performance, a
Kalman filter 212 is optionally used to reduce the E.sub.rs
estimation error. As previously mentioned, the respiratory system
elastance, E.sub.rs, typically does not change rapidly as a
function of time. The Kalman filter 212 is used to filter the
estimation noise in {tilde over (E)}.sub.rs(t) and improve the
E.sub.rs(t) estimation results. The inputs to the Kalman filter 212
are {tilde over (E)}.sub.rs (t) and .delta..sub.E.sub.rs(t). The
output of the Kalman filter 214 is the final estimate of
E.sub.rs(t), notated herein as E.sub.rs(t), and {tilde over
(E)}.sub.rs(t)=E.sub.rs(t)+.omega..sub.E (t) where .omega..sub.E
(t) is a noise or uncertainty metric. The above model assumes that
{tilde over (E)}.sub.rs (t) is an unbiased estimate of E.sub.rs(t)
that has a noise term .omega..sub.E(t).about.N(0,
.delta..sub.{tilde over (E)}.sub.rs(t)).
[0039] The Kalman filter can be designed to reduce the MWLS
estimation noise based on the following assumptions: (1) a state
process equation where E.sub.rs changes slowly and can be modelled
as a random walk, i.e. E.sub.rs(t)=E.sub.rs(t-1)+.omega..sub.E(t),
.omega..sub.E(t).about.N (0, .delta..sub.E); and (2) an observation
equation where the MWLS estimate {tilde over (E)}.sub.rs(t) can be
modelled as {tilde over (E)}.sub.rs(t)=E.sub.rs(t)+.omega..sub.LE
(t), .omega..sub.LE(t).about.N(0, .delta..sub.{tilde over
(E)}.sub.rs(t)). A standard Kalman filter can be implemented with
A=1, B=0, Q=.delta..sub.E, H=1, and R=.delta..sub.{tilde over
(E)}rs(t). The Kalman filter has certain advantages, including
computationally efficient implementation in the context of a
sliding time window, intuitive operation and output of weighted
averages. The parameter .delta..sub.E is an algorithm parameter
that controls the average window length.
[0040] With continuing reference to FIGS. 1 and 2, the final output
E.sub.rs (t) 214 of the E.sub.rs estimator 132 is utilized by the
succeeding R.sub.rs estimator 134 in performing the R.sub.rs(t)
estimation. To estimate R.sub.rs(t), the elastic pressure component
E.sub.rsV(t) is cancelled out of P.sub.y (t) using E.sub.rs(t).
This E.sub.rs cancellation operation 216 can be expressed as:
{tilde over (P)}.sub.y(t)=P.sub.y(t)-E.sub.rs(t)V(t)
The E.sub.rs cancellation 216 removes one unknown (E.sub.rs) from
the Equation of Motion of the Lungs, and thus simplifies the
R.sub.rs estimation. Assuming the estimation E.sub.rs(t) output by
the E.sub.rs estimator 132 is correct and the elastic pressure
component is perfectly cancelled, the MWLS operation 218 of the
R.sub.rs estimator 134 optimizes the equation:
{tilde over (P)}.sub.y(t)=.apprxeq.R.sub.rs{dot over
(V)}(t)+P.sub.mus(t)
Using the Moving Window Least Squares (MWLS) estimator 218, the
respiratory resistance R.sub.rs is estimated.
[0041] In the E.sub.rs estimator MWLS operation 210 of the E.sub.rs
estimator 132, the respiratory muscle pressure P.sub.mus(t) is
indirectly estimated as a linear function of t since the difference
of P.sub.mus(t), namely .DELTA.P.sub.mus(t), is estimated as a
constant value .DELTA.P.sub.mus for each time window. However, it
has been found herein that this estimate is unduly coarse in the
case of the MWLS operation 218 of the R.sub.rs estimator 134, and
that significantly improved estimation of the respiratory
resistance R.sub.rs is provided if the time dependence of the
respiratory muscle pressure P.sub.mus(t) is adaptively modeled in
the MWLS operation 218. In illustrative examples herein,
P.sub.mus(t) is modeled using a low order polynomial, e.g. of order
0 (constant value), 1 (linear), or 2 (quadratic). The order of the
P.sub.mus(t) polynomial function, M, can significantly change the
estimation performance.
[0042] With brief reference to FIG. 4, moreover, the optimal order
M of the polynomial used to model P.sub.mus(t) depends on the
position of the moving window 130 within the respiratory cycle. In
illustrative FIG. 4, the first time window 130.sub.A is located at
a respiratory phase for which a first order (M=1) polynomial is an
effective model of P.sub.mus(t); whereas, for the respiratory phase
at which the second time window 130.sub.B is located a zeroeth
order (M=0) polynomial is effective. However, the respiratory phase
at which the current time window 130 resides is generally not an
input to the R.sub.rs estimator 134.
[0043] With brief reference to FIG. 5, for the R.sub.rs MWLS 218,
the P.sub.mus(t) waveform is modeled as an M.sup.th-order
polynomial function (M>=0), i.e. P.sub.mus(t)=a.sub.0+a.sub.1t+
. . . +a.sub.Mt.sup.M, and the R.sub.rs(t) parameter is assumed to
be constant. (While a polynomial model of P.sub.mus(t) is described
herein for illustration, other models comprising a parameterized
function of time such as a spline model are also contemplated.) To
accommodate the differences in optimal polynomial order over the
respiratory cycle, the R.sub.rs MWLS estimator 218 calculates three
R.sub.rs estimates: an MWLS estimate 218.sub.0 using a
0.sup.th-order polynomial (M=0, i.e. P.sub.mus(t) is modeled as a
constant); an MWLS estimate 218.sub.1 using a 1.sup.st-order
polynomial (M=1, i.e. P.sub.mus(t) is modeled as a linear function
of t); and an MWLS estimate 218.sub.2 using 2.sup.nd-order
polynomial (M=2, i.e. P.sub.mus(t) is modeled as a quadratic
function of t). The MWLS formulation for each MWLS estimator
218.sub.0, 218.sub.1, 218.sub.2 is listed in the righthand box of
FIG. 5 as well as in Table 1 below.
TABLE-US-00001 TABLE 1 R.sub.rs estimator formulations for each
P.sub.mus(t) model P.sub.mus Moving Window Least Squares Model
Estimator 0.sup.th P.sub.mus(t) = {tilde over (P)}.sub.y(t)
.apprxeq. R.sub.rs{grave over (V)}(t) + P.sub.mus(t) Order a.sub.0
[{tilde over (R)}.sub.rs,0, a.sub.0].sup.T = (X.sup.T
X).sup.-1X.sup.TY Y = [{tilde over (P)}.sub.y(t), {tilde over
(P)}.sub.y(t - 1), . . . , {tilde over (P)}.sub.y(t - L + 1)].sup.T
X = [x(t), x(t - 1), . . . , x(t - L + 1)].sup.T x(t) = [{grave
over (V)}(t), 1].sup.T .delta..sub.{tilde over (R)}.sup.rs,0 =
(X.sup.T X).sup.-1(1, 1) * .delta..sub.Py 1.sup.st P.sub.mus(t) =
{tilde over (P)}.sub.y(t) .apprxeq. R.sub.rs{grave over (V)}(t) +
P.sub.mus(t) Order a.sub.0 + a.sub.1t [{tilde over
(R)}.sub.rs,.sub.1,a, a.sub.0, a.sub.1].sup.T = (X.sup.T
X).sup.-1X.sup.TY Y = [{tilde over (P)}.sub.y(t), {tilde over
(P)}.sub.y(t - 1), . . . , {tilde over (P)}.sub.y(t - L + 1)].sup.T
X = [x(t), x(t - 1), . . . , x(t - L + 1)].sup.T x(t) = [{grave
over (V)}(t), 1, t].sup.T .delta..sub.{tilde over (R)}.sup.rs,1 =
(X.sup.T X).sup.-1(1, 1) * .delta..sub.Py 2.sup.nd P.sub.mus(t) =
{tilde over (P)}.sub.y(t) .apprxeq. R.sub.rs{grave over (V)}(t) +
P.sub.mus(t) Order a.sub.0 + a.sub.1t + [{tilde over
(R)}.sub.rs,.sub.2, a.sub.0, a.sub.1, a.sub.2].sup.T = (X.sup.T
X).sup.-1X.sup.TY a.sub.2t.sup.2 Y = [{tilde over (P)}.sub.y(t),
{tilde over (P)}.sub.y(t - 1), . . . , {tilde over (P)}.sub.y(t - L
+ 1)].sup.T X = [x(t), x(t - 1), . . . , x(t - L + 1)].sup.T x(t) =
[{grave over (V)}(t), 1, t, t.sup.2].sup.T .delta..sub.{tilde over
(R)}.sup.rs,2 = (X.sup.T X).sup.-1(1, 1) * .delta..sub.Py
[0044] With continuing reference to FIG. 5, the three R.sub.rs(t)
estimates output by the respective MWLS operations 218.sub.0,
218.sub.1, 218.sub.2 are combined together by a combining operation
219 to produce the final MWLS estimate, {tilde over (R)}.sub.rs(t).
The combining operation 219 may use various combinational
techniques, such as a maximum ratio combination operation or a
minimum variance selection combination. The maximum ratio
combination employed by the illustrative combiner 219 assigns the
largest weight to the estimate with the least estimation variance
(e.g. the one with the best polynomial order) so that the one with
the best polynomial order will dominate the R.sub.rs estimation
output. The MWLS 218 also calculates the variance of {tilde over
(R)}.sub.rs(t), .delta..sub.{tilde over (R)}.sub.rs.
[0045] With returning reference to FIG. 2, in the case of the
R.sub.rs estimator 134 only the R.sub.rs(t) estimate, {tilde over
(R)}.sub.rs (t), output by the MLS operation 218 is retained while
the other estimation outputs (e.g. P.sub.mus(t) polynomial
coefficients) are discarded. In the final stage of the R.sub.rs
estimator 134, a Kalman filter 220 is applied to further improve
the R.sub.rs estimation output by the MWLS 218. The Kalman filter
220 is suitably similar to the Kalman filter 212 described above
with respect to the E.sub.rs estimator 132. The Kalman filter 220
for the R.sub.rs estimator 134 can be designed to reduce the MWLS
estimation noise based on the following assumptions: (1) a state
process equation where R.sub.rs changes slowly and can be modelled
as a random walk, i.e. R.sub.rs(t)=R.sub.rs(t-1)+.omega..sub.R(t),
.omega..sub.R(t-1).about.N(0,.delta..sub.R); and (2) an observation
equation where the MWLS estimate {tilde over (R)}.sub.rs(t) can be
modelled as {tilde over
(R)}.sub.rs(t)=R.sub.rs(t)+.omega..sub.LR(t), where .omega..sub.LR
(t).about.N(0, .delta..sub.{tilde over (R)}.sub.rs(t)). A standard
Kalman filter can be implemented with A=1, B=0, Q=.delta..sub.R,
H=1, and R=.delta..sub.{tilde over (R)}rs(t). Again, the Kalman
filter has certain advantages, including computationally efficient
implementation in the context of a sliding time window, intuitive
operation and output of weighted averages. The parameter
.delta..sub.R is an algorithm parameter that controls the average
window length.
[0046] The output 222 of the R.sub.rs(t) Kalman filter 220 is the
R.sub.rs estimate notated here as {circumflex over
(R)}.sub.rs(t)=R.sub.rs(t)+.omega..sub.r(t). This output assumes
that {tilde over (R)}.sub.rs(t) is an unbiased estimate of
R.sub.rs(t), but has a noise term .omega..sub.r (t).about.N(0,
.delta..sub.{tilde over (R)}.sub.rs).
[0047] With reference back to FIG. 2, in the final pass, once the
E.sub.rs and R.sub.rs estimates are obtained by the respective
estimators 132, 134, the P.sub.mus(t) estimator 136 is applied to
estimate P.sub.mus (t). Using the previously estimated R.sub.rs(t)
and C.sub.rs(t), a P.sub.mus(t) computation 224 computes the {tilde
over (P)}.sub.mus(t) estimate according to:
{tilde over (P)}.sub.mus(t)=P.sub.y(t)-E.sub.rs(t)V(t)-{circumflex
over (R)}.sub.rs(t){dot over (V)}(t)
evaluated over the samples of P.sub.y (t), {dot over (V)}(t), and
(via integrator 122) V(t) in the time window 130. Said another way,
{tilde over (P)}.sub.mus(t)=P.sub.y (t)-{circumflex over
(R)}.sub.rs{dot over (V)}(t)-E.sub.rs V(t) is evaluated in the time
window 130 of the MWLS. To remove high-frequency noise in {tilde
over (P)}.sub.mus(t), an optional low-pass filter 226 can be used
to further improve the P.sub.mus(t) estimate. Additionally or
alternatively, physiological knowledge of the P.sub.mus(t) waveform
can be infused to further improve the P.sub.mus(t) estimation.
[0048] In the illustrative embodiments, the respiratory elastance
(or compliance) estimator 132 is applied first, followed by the
respiratory resistance estimator 134 and finally the respiratory
muscle pressure estimator 136. However, it is contemplated to
estimate the respiratory resistance first, followed by estimation
of the respiratory elastance or compliance (that is, to reverse the
order of the estimators 132, 134). In such a variant embodiment,
the second (E.sub.rs) estimator would suitably include an R.sub.rs
cancellation operation analogous to the operation 216 of the
illustrative embodiment. Regardless of the order of estimation of
E.sub.rs (or C.sub.rs) and R.sub.rs, it will be appreciated that
the final P.sub.mus(t) estimator 136 could optionally be omitted if
P.sub.mus(t) and WoB (computed therefrom by integrator 140) are not
used.
[0049] If the respiratory elastance (or compliance) and/or
resistance are displayed on the display component 110 of the
ventilator 100, these values may optionally also be displayed with
their respective uncertainty metrics, for example expressed in
terms of the .delta. or .omega. statistics described herein or
functions thereof. While these or other respiratory parameters are
described as being displayed on the display component 110 of the
ventilator 100 in the illustrative examples, it will be appreciated
that such values may additionally or alternatively be displayed on
a bedside patient monitor, at a nurses' station computer, and/or
may be stored in an Electronic Health Record (EHR) or other patient
data storage system, or so forth. The illustrative respiratory
system analyzer 120 is suitably implemented via the microprocessor
of the ventilator 100; however, the respiratory system analyzer 120
could additionally or alternatively be implemented via a
microprocessor of a bedside patient monitor or other electronic
data processing device. The disclosed respiratory system analyzer
functionality may also be embodied by a non-transitory storage
medium storing instructions that are readable and executable by
such a microprocessor or other electronic data processing device to
perform the disclosed functionality. By way of example, the
non-transitory storage medium may, for example, include a hard disk
or other magnetic storage medium, optical disk or other optical
storage medium, flash memory or other electronic storage medium,
various combinations thereof, or so forth.
[0050] As previously noted, in addition to displaying one or more
of the estimated values (e.g. one or more of the values
E.sub.rs(t), C.sub.rs(t)=1/E.sub.rs, {circumflex over
(R)}.sub.rs(t), {circumflex over (P)}.sub.mus(t) optionally with
its statistical uncertainty) as a real-time value, trend line or so
forth, in another illustrative application the {circumflex over
(P)}.sub.mus(t) waveform may be used to synchronize the positive
pressure applied by the ventilator 100 with respiratory effort
expended by the patient 102, so as to reduce patient-ventilator
dyssynchrony. In this application, the positive air pressure
applied by the ventilator 100 is adjusted, e.g. increased or
decreased, in synch with increasing or decreasing magnitude of
{circumflex over (P)}.sub.mus (t). In another control application,
the WoB output by the integrator 140 may be used as a feedback
signal for control of the ventilator 100. In general, the positive
pressure applied by the ventilator 100 should increase with
increasing measured WoB output by integrator 140, and this
increased mechanical ventilation should result in a consequent
reduction in patient WoB until the setpoint WoB is reached. As
illustration, a proportional and/or derivative and/or integral
controller (e.g. PID controller) may be used for this feedback
control with the WoB signal from the integrator 140 serving as the
feedback signal, a target WoB serving as the setpoint value, and
the positive pressure being the controlled variable.
[0051] The respiratory system analyzer 120 has been tested with
simulated data and with pig respiratory data, and the results show
the analyser 120 can provide comparable results to invasive
solutions and is stable under different ventilator settings,
including low PSV settings. The analyzer 120 provides various
benefits, including (but not limited to): providing real-time data
(with a lag of a few seconds or less); sample-by-sample estimation
(if successive windows overlap and are spaced by a single sample);
tailorable trade-off between computational complexity and temporal
resolution (faster computation by larger spacing between possibly
overlapping windows traded off against reduced temporal
resolution); rapid convergence (within 10 breaths in some tests)
providing low initiation time; stability against unexpected
disturbances; good computational efficiency employing, for example,
efficient pseudo-inverse (L.times.4) matrix computation (where L is
the window size, e.g. 60-90 samples in some suitable embodiments);
and low memory requirements (storing the data for the current time
window, around 60-90 samples for some embodiments).
[0052] As a further advantage, the respiratory system analyzer 120
suitably estimates the elastance or compliance E.sub.rs(t),
resistance R.sub.rs(t), and respiratory muscle pressure
P.sub.mus(t) without receiving as input the respiratory phase or
respiratory rate, and without making any a priori assumptions about
these parameters (other than that E.sub.rs and R.sub.rs are assumed
to be constant within any given time window of the MWLS
estimation). The respiratory system analyzer 120 suitably operates
only on the measured air pressure P.sub.y(t) and air flow {dot over
(V)}(t) along with V(t)=.intg.{dot over (V)}(t)dt which is derived
by integrating {dot over (V)}(t) over time.
[0053] The invention has been described with reference to the
preferred embodiments. Modifications and alterations may occur to
others upon reading and understanding the preceding detailed
description. It is intended that the invention be constructed as
including all such modifications and alterations insofar as they
come within the scope of the appended claims or the equivalents
thereof.
* * * * *